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The business world today is moving at a much faster pace, and rapid changes in competition, demand, technology etc. have made it more critical than ever for consumer brands to be able to respond to changes quickly. But according to a recent McKinsey Survey, organizational agility the ability to quickly react to change and move toward value-creating and value-protecting opportunities is elusive for most.

With these examples its clear the battle for the billions of consumers on the planet is tougher and while organizations understand the important of becoming leaner and quicker they are not yet there. In the McKinsey survey, when asked where their companies apply agile ways of working, respondents most often pointed to areas that are customer focused such as innovation, customer experience, sales and servicing, and product management.

The common them to better customer focused operations is accurate data and insights on the consumers. There is so much data out there today so there is no shortage of it but mining it and making it available easily to a wide range of people who need it within the business is still challenging for most. And this is what we will cover here – how you can take steps to do just that, deliver actionable data to a wide range of business users to become more agile with better, data driven decisions.

Step 1 to agility – Think different!

With 90% of the world’s data created in the last 2 years the world data is growing at a scary pace…there are so many ways now for consumers to share data and information that organizations everywhere need to analyze and deal with textual data. With so much data – methods and technologies that were created over 2 years ago are not setup to handle the new world. You need to start thinking differently and walk away from last year’s tools you used like surveys or human experts-based solutions.

Step 2 to agility – Automation

With so much information available online and in house, human based solutions, even AI driven systems that rely on IT and experts to setup, are too slow. IT and data scientists will just not be able to respond quickly enough to every business data request while in parallel they still need to fine tune and configure a data mining system to respond to competitor and market changes. The key is automation. Find the automated data mining systems that can harvest the insights without delays. Fortunately, they exist now.

Step 3 to agility – Personalize

If you figured out a way to access insights and mine data, you need to keep in mind that generic, one size fits all data is not usable by all. Specific roles need specific data. A Product Manager needs granular data on the product that he is selling, not on the category or the brand. This is why you need data that anyone can slice and dice for their own use. Democratization of data is key to distribution of decision making.

Step 4 to agility – Simplify

Similar to the previous step #3, if you want to encourage a wide use of data across roles in the organization the solution of choice needs to be one that everyone can use. It needs to be intuitive, and autonomous. If the solution is complex or if it requires IT or Insights or any other centralized group to change or support or configure. You want to empower the masses to take action and they can’t take it if they don’t have control

Step 5 to ace your launch – Spread the joy (Distribute)

So if you’ve reached here hopefully you have data that is easily accessible. However in large consumer brands this data will likely be used in meetings or to be sent to others in the organization. Whatever you can do to simplify the distribution part will highly reward you. Setting up one click reports or circulating scheduled reports over email are some examples of how you can make things easier for the team. Obviously export to PowerPoint or Excel can’t hurt either.

Conclusion

The battle for the modern consumer is already taking place, and agile organizations are the ones that will survive. A key aspect to becoming truly agile is to empower your peers to make accurate, data driven decisions, on a wide scale of roles in the organization.

According to Harvard Business School about 95 percent of new consumer products fail. The problem often is that their creators are using an ineffective market research. Harvard Business School encourages consumer brands to look at products the way customers do: as a way to get a job done. In this post we’ll offer (based on our experience) the 5 steps to acing a consumer product launch.

Anatomy of a failed product launch

In our experience at Revuze, consumer brands repeatedly fall into the following patter that causes them to have problematic product launches, and lengthy failure cycles post launch:

Brands rely on narrow research to plan a new product launch

Narrow research + bugs in a completely new product = post launch issues

New product launch hype causes consumer euphoria for first few weeks

Consumer brands falls in love with the euphoria and lowers its guard

Product issues start to surface 3-6 weeks post launch

Consumer brands can take up to 4-7 months to figure and fix new issues

Step 1 to ace your launch – Research

With so much information available online and in house, use AI driven systems to mine the data and make sure you understand in details:

The competition – existing products, capabilities, what buyers like and don’t like about them…

Key trends – what is hot, what is considered innovative

Step 2 to ace your launch – Build your messaging

Messaging is mostly about refining your product narrative to focus on only the most valuable aspects of the new product via a simple message. Its challenging but is critical to make sure you answer the core needs of your audience. For example if you want to launch a new product that compares to a competitor product you need to understand the weaknesses of the competitor so you can go after them in your messaging. Similarly if you launch a new product and want your audience to upgrade to it from their existing product you want to emphasize why they should do so, what are their core benefits

Step 3 to ace your launch – Baseline

To make sure you fully understand market behavior post launch you need to sample the market behavior before your launch and make sure you have it nailed down. You want to know the competitor brands and product momentum, sentiment, strengths and weaknesses. Post your launch you will want to be able to monitor those aspects and derive from them your success and failure points. Where you succeed you will see a decline for your competitors. If you don’t see it or don’t see enough of it – it could be early signs of a problem

Step 4 to ace your launch – Analyze

Once your product is out you need to analyze like crazy, as the hype cycle means you will mostly get positive data back – great news coverage, great feedback form happy customers who just bought your product but have not used it for a long period of time, even feedback from promotions and incentivized reviews (see our latest research report – The pros and cons of incentivized reviews). The problems will surface, there is no question about it. You will need to continue to monitor the feedback coming from the market (reviews, social media, call center, emails, website, etc) for the early warning signs

Step 5 to ace your launch – Repeat

It’s a never ending cycle impacted by many factors so you always need to stay on top of your product. You will need to fix it, revise it, upgrade it and match it with new competitors. At all times the thing that will help you the most is to keep understanding your buyers, competitors, trends so you are always on top of where your specific product is and what you need to do next and why so you keep ahead of the market

To chase a moving target you need a better mousetrap

So we need to track social media, eCommerce, call center, in store data, open ended surveys and what not to stay on top of a product and a market with competitors, for that we will need some serious software that can be:

It has to be self learning

Able to identify topics and interests

Able to understand that different terms talk about the same topic

Able to learn new terms that surface and their meaning

Simple for a wide range of business users to use

Luckily we at Revuze invented the first all automated market research software – zero effort and no experts or IT needed!

Conclusion

Launching a new consumer product is practically guaranteed to fail as research shows. Luckily new technologies can help you prep and monitor a launch so you can ace it. You’ll need to skip the hype phase/cycle though to cross the chasm to success. You will also need to make sure you don’t miss data sources as hints of failures can come from everywhere. The good news is that help is around, if you know what to look for

Revuze is honored to be invited to speak at IIeX Atlanta by Procter &amp; Gamble. As a Procter &amp;
Gamble supplier of consumer insights technology Revuze partnered with Procter &amp; Gamble
across many product areas to win its confidence.
Revuze is the first automated market research technology, turning unstructured text from
eCommerce sites, Social Media, call centers, emails, surveys etc. into a complete market
research analysis, covering brands to products to features, without any involvement of IT or
data scientists or experts.
With such a unique technology any employee of Procter &amp; Gamble that needs deep market
information to make daily business decisions is empowered to do so with high level of
confidence and without the need for lengthy cycles of research, analysts or IT projects.
During this session our team will describe the recent emerging trends that enable turning every
employee into a seasoned analyst as well as the technological breakthroughs that enabled the
Revuze innovation. This will include:
 90% of the world data was created in the last 2 years per IBM
 Most of the answers brands look for are already there
 Mining the data today required experts, IT and delivers partial (&lt;20%) results
 AI and ML offers the building blocks to remove the humans from the mining effort
 Machines are much more accurate (Thus Revuze can deliver ~90% accuracy) and scalable
 How Revuze delivers 5-10X more granularity to brands so they can make well educated
decisions
 Sample use benefits across Product, Marketing, Research &amp; QA

Please come see us sharing the stage with Procter &amp; Gamble and see firsthand how you, too,
can empower your brand employees to make better, educated decisions, on a daily basis.
The session description is at https://iiex-na.insightinnovation.org/agenda/session/247046 and
the conference website is at https://iiex-na.insightinnovation.org/ . The conference takes place
in Atlanta between 11-13 of June, 2018 and our session with Procter &amp; Gamble is on Monday
June 11 th at 3pm ET.
Looking forward to seeing you all there and share the next Consumer Insights revolution with
you all!

This year (2018), an estimated 3.2 billion people will be using social media worldwide. With this magnitude there is no question every brand wants to know what is said about it over social media. Its common knowledge that that’s where consumers express themselves and following what your consumers are saying about you is good practice. Just to look at a couple of data points about the sheer volume of information on social media –

With lots of data comes lots of …. Noise! Apparently when brands look to leverage the social data and its massive amounts of information they typically find that the majority of it is either useless or shallow.

What are brands looking to learn?

With social listening brands are typically trying to decipher social content deep enough to be able to achieve the following goals

Just so you know, there’s a lot of time and effort going into social listening, its not plug and play…You’d think with all this data available all you have to do is just mine it…but how would you? You could use text analytics software and teach it what keywords and terms to look for. Its challenging as you need to guess a lot:

Guess ALL the different ways that different people talk about the same thing: If we take the example of a phone battery, you’d need to look for all positive and negative expressions about it such as “my phone lasts for days” or “mine doesn’t hold charge for more then 4 hours” or “my phone’s battery is weak”

Guess the discussion topics that are not on your mind: How can you guess ALL the different topics consumers talk about? The short answer is you can’t, which means you are missing quite a bit

Guess the next hot topic: Consumers interests shift all the time based on new products, features or change in flavors. You’d need to know something has come up in order for you to look for it, and then obviously you need to guess the different keywords used by the masses to describe it

Mapping topics to products and services of yours: Once you actually mapped topics, how do you know which product or service of yours they relate to? There is no SKU. You can try to search for product names but you can’t really rely on your audience to accurately put it in

In summary on the effort part there is a lot of manual setup effort and at the end you can’t really get granular results, you just know basic sentiment and general popular topics, but without the deep (Product/Service) context associated with it.

To chase a moving target you need different methods

Training systems to look for rigid keyword patterns that humans guess in hope that we cover all that consumers talk about in social media seems futal. Lets try to spell out what type of systems can actually track a dynamic, moving target of topics:

It has to be self learning

Able to identify topics and interests

Able to understand that different terms talk about the same topic

Able to learn new terms that surface and their meaning

With Artificial Intelligence and Self Training algorithms you can skip the person-training-machine step which is the most limiting one.

Conclusion

With social listening the goals are pretty well defined, and most of them are shallow and easy to achieve –

Reputation defense

Brand sentiment

Identify influencers

The key challenge is to drive meaningful insights from the masses of social data that you have, and for this, most text analytics technologies that rely on humans are not enough. Consumer interests are too much of a fast moving target.

The good news is that the data is there for us to see and mine, assuming we can get to it.

Revuze is an innovative technology vendor that addresses just this with the first self training, low touch solution that can mine data automatically. This is why its much more granular and typically delivers 5-8X the data coverage compared to anything else, and it does it without humans helping.

With the incredible ease of online shopping comes a huge retail and CPG problem – a growing rate of returned products. It’s not uncommon to see return rates of 30% or more for merchandise that’s bought online. Clothing returns can be around the 40% mark.

In total, Americans returned $260 billion!!! in merchandise to retailers last year, or 8% of all purchases, according to the National Retail Federation. That 8% grows to 10% during the holiday season.

With eCommerce now an inseparable part of every retailer and CPG maker, how can you effectively understand your returns to battle the trend and lower the rate of returns?

In a recent article, Tom Enright, supply chain research director at Gartner commented on this phenomenon saying, “Retailers are not very good at managing returns right now, and so unless they invest in their ability to manage returns, the volume of returns coming back will cause problems in their overall supply chain,”. He warns retailers that dealing with returns the old way is a “ticking time bomb turning into a major cash hole.”

To lower returns you need to know your data

To battle your returns, no matter if you’re a CPG maker or a retailer, you need to understand the reasons for the returns so you can take action. In general returns are around the following reasons:

Product fit / Product doesn’t match the description

The customer may have bought the wrong item or bought an item that doesn’t fit his exact need. This is more common for ecommerce as consumers can’t physically see or feel the product.

Gift returns

This is pretty obvious, you get a gift that you don’t need or can’t afford to keep

Product defects

This is the area that is most controllable. Since if you know what the defects are you can fix them or work around them to reduce returns

Where does the data about return lies? If you’re a retailer you should have

In store data from the return desk

Online data from online returns

Call center data

emails

If you’re a CPG maker sources vary slightly:

eCommerce comments and reviews on sites like Amazon or major retailers websites

The CPG website comments and reviews

Call center data

emails

The challenge with all this data is that it’s very hard to mine it. It’s a lot of data, and mining it relies on having a team of experts using text analytics software to look for keywords and terms. Its been done quite a bit, though its challenging as it’s a high effort low return exercise as you need to guess quite a bit:

Guess ALL the different ways that people talk about the same return issue or product defect: Imagine how kids, teenagers, boys, girls, adults of different ages all speak about the same topic. If we take the example of an electrical appliance battery, you’d need to look for all negative expressions about it such as “mine doesn’t hold charge” or “battery is weak” or “it dies on me after minutes of usage”

Guess the topics that are not on your mind: How can you guess ALL the different topics consumers talk about? The short answer is you can’t, which means you will miss quite a bit

Guess the next hot topic: With new products comes new issues. You’d need to know something has come up in order for you to look for it, and then obviously you need to guess the different keywords used by to describe it

So how can you make it work across lots of data

At Revuze we offer the first all automated, zero setup, consumer insights solution:

It is self training

It automatically identifies topics and interests

It automatically understands that different terms talk about the same topic

It learns new terms that surface and their meaning

Around this we built other capabilities that help CPGs and retailers understand the key product issues out of heaps of data and handle them effectively:

Alerts for new major issues when analyzing your data

Deep analysis of the product issues description to suggest root cause

One click reporting to share the issue with product or return teams

With the above capabilities a Fortune 500 CPG maker is able to identify product issues with a team of 8 people that manages issues across 5000 product lines:

New issues are surfaced automatically through alerts

The person getting the alert analyzes suggested root cause

One click reporting sends the information onward to product or logistics teams

Conclusion

Product returns is the new epidemic for ecommerce and is hitting retailers and CPGs hard. To battle it they need to be able to control their data, and it can’t be done with humans, experts or data scientists. Its just not good enough.

Revuze is an innovative technology vendor that addresses just this with the first self training, zero setup, low touch solution that mines the data for you, and it does it without humans. Ping us to learn more…

According to a recent research 97 percent of customers said they had read reviews in 2017. There are so many ways now for consumers to share data and information that online consumer reviews and feedback data is in fact become the world largest consumer panel. And because it is an anonymous one it’s easy to share loads and loads of data since no one is worried about saying the wrong thing. This has contributed to the explosion of world data to the point that as IBM recently pointed out, 90% of the world’s data was created in the last 2 years.

In fact, its even better then just the world largest consumer panel, as consumers are not concerned that they are listened to and as such convey their opinions more freely…

With so much data available on such a diversified set of consumer goods/services topics – why aren’t all consumer brands listening to it? There’s so much good data available in areas such as customer service and QA (returns, complaints, failures, missing parts, packaging), product or service (popular features, negative reviews, competitive analysis) and market research (analyzing brands, products and sentiment and looking for white spaces for innovation).

It’s a (huge) moving target?

You’d think with all this data available all you have to do is just mine it…but how would you? You could use text analytics software and teach it what keywords and terms to look for. Its been done quite a bit, though its challenging as you need to guess a lot:

Guess ALL the different ways that different people talk about the same thing: Imagine how kids, teenagers, boys, girls, adults of different ages all speak about the same topic. Would you expect it to be with the same exact words? If we take the example of a phone battery, you’d need to look for all positive and negative expressions about it such as “my phone lasts for days” or “mine doesn’t hold charge for more then 4 hours” or “my phone’s battery is weak”

Guess the discussion topics that are not on your mind: How can you guess ALL the different topics consumers talk about? The short answer is you can’t, which means you are missing quite a bit

Guess the next hot topic: Consumers interests shift all the time based on new products, features or change in flavors. You’d need to know something has come up in order for you to look for it, and then obviously you need to guess the different keywords used by the masses to describe it

To chase a moving target you need different methods

So training systems to look for rigid patterns that humans invent in hope that we cover all that consumers talk about seems hopeless. Lets try to spell out what type of systems can actually track a dynamic, moving target of topics:

It has to be self learning

Able to identify topics and interests

Able to understand that different terms talk about the same topic

Able to learn new terms that surface and their meaning

With Artificial Intelligence and Self Training algorithms you can skip the person-training-machine steps which limit the scope of the machine understanding and is also slow in terms of response time and skip directly to a machine-training-machine scenario, growing to unlimited scale and immediate response to any variation of a meaning.

Conclusion

Most text analytics technologies rely on humans and thus are slow to setup and mainly they miss a lot of the meaning as consumers interests is a fast moving target.

The good news is that the data is there for us to see and mine, assuming we can get to it.

Revuze is an innovative technology vendor that addresses just this with the first self training, fast setup and low touch solution that typically delivers 5-8X the data coverage compared to anything else, and it does it without humans, in a self training solution that can adapt to the moving target of consumer interests

There’s a new Revuze research report in town! Comparing incentivized reviews to non-incentivized ones to highlight the differences in score and content that you get from each. Below are some of the report highlights and to read the full report just contact us. Enjoy:

According to a recent research 97 percent of customers said they had read reviews in 2017. This is no secret that online reviews are skyrocketing as consumers share more and more data. As IBM recently pointed out, 90% of the world’s data was created in the last 2 years.

With so many online reviews out there, we’ve decided to take a look at how incentivized consumers’ reviews fair against regular reviews from consumers that were not incentivized. Is one better than the other? More positive? Are they similar in topics covered and level of details? Stay tuned to find out…

But first, what is an incentivized review?
An incentivized review is a review written by a consumer who got the product for free or got a coupon for the review. This typically means that the consumers writing the reviews are pushed to write them close to the initial use of the product and you’d also expect these “biased” consumers to be more positive.

Methodology

We’ve used our Revuze dashboard to analyze data collected from the Razors and Blades industry. Of course, the results changes per industry, per brand and per product but since this is based on a substantial sample of data (Over 300,000 reviews) we assume it will be representative.
How Revuze work?
Revuze is an automated market insights solution that can take any type of unstructured data about consumer products and through deep understanding of consumer intent use it to understand an entire market – brands, product and features reviewed. Data sources include online reviews, emails, social media, call transcripts etc.

When incentivized reviews are included, these reviews are marked as incentivized either by the reviewers in the review body OR the eCommerce site might also mention it (e.g. “This review was taken as part of a promotion”).

Leveraging its deep consumer understanding Revuze technology detects all the possible variations of how consumers specify that this is an incentivized review (“got coupon”, “part of promotion” etc) and marks these reviews as incentivized reviews.

High level conclusions

Comparing over 300,000 incentivized and non-incentivized reviews leads us to the following conclusions (again, limited to a specific market and product):

Incentivized reviewers are more positive towards the product (Contact us to get the full report)

Incentivized reviewers mention that they received this product as part of promotion

Overall positive incentivized reviews do not tend to evaluate the price of the product. In the non-incentivized reviews this aspect is included in the top 10 most evaluated topic while in the incentivized positive reviews it is not in the top 10 mentioned topics

Based on the above we highly recommend that when you analyze consumer feedback you will separate incentivized reviews from non-incentivized reviews as the two audiences behave pretty differently.

With 90% of the world’s data created in the last 2 years the world data is growing at a scary pace…there are so many ways now for consumers to share data and information that organizations everywhere need to analyze and deal with textual data. Obvious examples are customer service (returns, complaints), QA (failures, missing parts, packaging), product (popular features, negative reviews, competitive analysis) and market research (analyzing brands, products and sentiment).

With so much text to look into it just make sense to leverage technology to help you slice it into buckets and areas of interest. This is where Text Analytics and Natural Language Processing (NLP) come it.

So what are Text Analytics or NLP?

Text analytics (Sometimes referred to as text data mining) is the process of deriving high quality information from text. This is typically achieved through finding patterns and trends by means such as statistical pattern learning. Text analytics usually involves structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and inserting into a database), deriving patterns within the structured data, and finally evaluation and interpretation to make meaningful observations. Text analytics typically doesn’t involve the semantics in the text and is more about text patterns discovery.

NLP is a component of text analytics that performs a special kind of linguistic analysis that helps a machine “read” text. NLP is about understanding Natural Language, as Natural language is what humans use for communication. The data could be speech or text and as such the main goal is to understand what is the semantic meaning of it.

NLP and text analytics are complimentary, where typically text-mining uses NLP, because it makes sense to mine the data when you understand the data semantically

How does NLP work?

First, the computer must understand what each word is. It tries to understand if it’s a noun or a verb, if it’s past or present tense, and so on. This is called Part-of-Speech tagging (POS).

NLP systems also have a vocabulary and a set of grammar rules coded into the system. Modern NLP algorithms use statistical machine learning to apply these rules to the natural language and determine the most likely meaning behind what was said.

The end goal is to have the computer understand the meaning of what was said/written. This is challenging as some words may have several meanings (polysemy) or different words having similar meanings (synonymy), but developers encode rules into their systems and train them to learn to apply the rules correctly.

So where is the problem?

The short answer is humans training NLP systems to “read” natural language. They put in a vocabulary and set of rules for the software to look for these words as a way to figure out meaning. The problem is that language is constantly evolving, and younger people create new ways of expressing yourself around a topic that didn’t exist before. How can you train a machine to look for something that doesn’t exist yet? Obviously once you realized that there is a new way to talk about a topic you now need to bring back the experts to train the system again to recognize the new keywords, which is time consuming and likely costly. At the end what it means is that you missed the bus…by the time you realize there is a new way to talk about something that is important to you likely the train had left the station and you missed the

meaning of this.

Lets pick and example. Lets say we’re a smartphone brand and want to analyze what consumers are saying about our latest phone’s battery life. We can try to scan online reviews and search for variations of the word “Battery”, but what happens if consumers are using phrases such as “doesn’t last long enough” or “phone died on me in the middle of the work day”?

What’s the right way to do things?

With Artificial Intelligence and Self Training algorithms you can skip the person-training-machine steps which limit the scope of the machine understanding and is also slow in terms of response time and skip directly to a machine-training-machine scenario, growing to unlimited scale and immediate response to any variation of a meaning.

Conclusion

Current NLP technologies rely on humans and thus are slow to setup, miss a lot of the meaning

The good news is that now there is enough data to make sure you can get answers to your questions, and all you need is just to analyze the data. Revuze is an innovative technology vendor that addresses just this with the first self-training, fast setup and low touch solution that typically delivers 5-8X the data coverage compared to anything else, and it does it without humans…

According to a recent report by IBM’s Marketing Cloud, 90% of the world’s data was created in the last 2 years!!! Isn’t it amazing? The world had grown 10X in data in 730 days at a rate of 2.5 quintillion bytes of data a day!…there are so many implications for this – where do you save all this data, how do you manage it, is there such a thing as too much data? What does it mean for the future? Will we have 10X data growth again in 2 years?

How do you shift your market research initiatives to handle such data volumes?

Using old Market research in a new world?

Market research used to be about data sampling and focus groups and surveys and in general peeking through the market peephole and estimating the overall market behavior and preferences based on a small group of individuals.

As data became more prevalent business started to use systems that can search data, but keep in mind these systems were set to search data that was likely 1/20 or so of what exists today.

So basically in the “old world” you either used brute manual effort or systems that could handle some data but most likely much less data then exists today.

How to process lots of data?

When processing lots of data, the data is typically unorganized (Unstructured is the commonly used term). Data can come from every brand encounter with consumers – emails, calls, surveys, website feedback….it also is available in the public domain online on eCommerce sites, review sites, social media etc.

This is a lot of data…sometimes in different languages, with a lot of different formats! To tackle it you need several core competencies:

Deep understanding of languages

Ability to relate data to a topic

Learn to recognize latest ways to talk about a topic

Understanding of sentiment

What is typically lacking in “old world” systems

Mainly autonomous decision making. When you need to go through lots and lots of data, you can’t rely on humans. Hoping humans will setup a computer system to analyze and handle any type of data is unrealistic. There are so many variations in the way people express themselves around a brand or product or feature that you can’t expect one person or even a team to figure all of these up. On top of it add languages, different data formats, ways consumers express sentiment and complexity just grows and grows…

Ideally if we wanted a technology that helps us handle unlimited data it would have to be one that can easily scale to multiple languages and data formats, automatically decipher the topics your consumers are talking about, automatically recognizes sentiment and can sum it all up for you.

Why is this difficult

Let’s pick and example. Let’s say we’re a smartphone brand and want to analyze what consumers are saying about our latest phone’s batter life. We can try to scan online reviews and search for variations of the word “Battery”, but what happens if consumers are using phrases such as “doesn’t last long enough” or “phone died on me in the middle of the work day”?

Conclusion

Current market research technologies rely on humans and thus are slow to setup, miss a lot of things and are slow to adapt. In a world that generates more and more data each year, and the data grows so quickly, you can’t rely on humans or manual labor to figure things out.

The good news is that now there is enough data to make sure you can get answers to your questions, and all you need is just to analyze the data. No more need for feedback groups, surveys etc.

The sad news is that most tools out there to help you do this were not build for this task. Revuze is an innovative technology vendor that addresses just this with a self-learning, fast setup and low touch solution that typically delivers 5-8X the data coverage compared to anything else, and it does it without humans…

According to a recent IBM research by 2020 US analyst and data jobs will grow 15% to a whopping 2.35M positions! It seems the more data there is we need more people to handle the data, especially market data. Isn’t something wrong with this picture? The more technology we have, better computers, more software options, smart machines – we still need more and more people? What do we need in terms of technology to be able to handle market feedback in a more efficient way?

Why is it so complex?

Market research is about processing lots of data. The data is also very much unorganized (Unstructured is a commonly used term). Data comes basically from every brand encounter with consumers – emails, calls, surveys, website feedback….it also is available in the public domain online on eCommerce sites, review sites, social media etc.

This is a lot of data…sometimes in different languages, with a lot of different formats! To tackle it you need several core competencies:

Deep understanding of languages

Ability to relate data to a topic

Learn to recognize latest ways to talk about a topic

Understanding of sentiment

Deep understanding of languages

As brands become global so are their consumers. Reviews and feedback can be provided in any number of languages and markets. Deciphering this feedback requires command of the languages in the markets where the brand sells. The larger the brand typically it will open up more markets and this in return will cause the brand to need more capabilities in new languages supported.

So if we wanted a technology that helps us mitigate this specific point it would have to be one that can easily scale to multiple languages and data formats.

Ability to relate data to a topic

As humans, we can’t process large amounts of data. If a brand has 50,000 feedback data points a month about a product (600,000 a year – which is not outrageous), we wouldn’t expect a person to review these data points, memorize them and summarize them to peers. Its just too much. We need the help of technology. But what type?

Most intelligent text processing technologies out there rely on people (hence the growing number of analysts) to define these groups of topics. Typically a core of 8-12 topics that are common practice such as Price, Service, Quality etc. But consumers are not limited to these topics, which means lots of data is left out of the feedback circle.

Ideally, we need here technology that can automatically decipher the topics your consumers are talking about and serve them back to you without human prep/bias.

Learn to recognize latest ways to talk about a topic

Another issue with the topic recognition setup by humans is to recognize new ways to talk about something. Millennials and newer generations keep inventing new ways to express themselves. A product can be “cool”, “good”, “great”, “solid” or “dope” – how do we keep up? One way is to continue to rely on humans to learn the new phrases, implement them into systems and track the new topics. Its time consuming, meanwhile we may miss market feedback or opportunities, and it requires us to keep piling up analysts…

If we wanted a technology that helps us mitigate this specific point it would have to be one that can learn to recognize new ways of saying “good” or “bad” as well as new discussion topics worthy of brand attention.

Understanding of sentiment

Similar to the previous clause, sentiment can be described in many ways/formats/languages and sometimes feedback also lacks sentiment…to be able to correctly identify and keep up with feedback you need a flexible way to pick up on new forms of sentiment as they appear (and not in retrospect) and also know to recognize when there is no sentiment included.

Conclusion

Current market research technologies rely on humans and thus are slow to setup, miss a lot of things and are slow to adapt. Revuze is an innovative technology vendor that addresses just this with a self learning, fast setup and low touch solution that typically delivers 5-8X the data coverage compared to anything else, and it does it without humans…